Filtering, State Estimation, and Other Forms of Signal Processing

14 Mar 2013 22:18

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Filtering. Is the Kalman filter like the EM algorithm, in any meaningful sense? (Yes; where'd that paper by Gharamani showing this go?) What is the Wonham filter, exactly? What are the estimation properties of the parameters in these filters? How bad is it to use the standard linear filters (Wiener, Kalman) on nonlinear systems? What do the existing nonlinear filters look like? (Hidden Markov models are one class of nonlinear filter; they have various drawbacks, mostly about needing to choose the architecture a priori, and it being hard to tell if you're using the wrong architecture, or the process is just intrinsically ugly.)

Nonlinear filtering.

Independent component analysis.

See also: Control Theory; Information Geometry; Monte Carlo and Stochastic Simulation; Time Series

Recommended, big picture:
• Nasir Uddin Ahmed, Introduction to Linear and Nonlinear Filtering for Engineers and Scientists [Clear introductory treatment with not-too-rigorous use of advanced probability theory, which is necessary to really explain what is going on and why it works for nonlinear and/or continuous-time signals.]
• R. W. R. Darling, Nonlinear Filtering --- Online Survey
• Neil Gershenfeld, The Nature of Mathematical Modeling, Part III
• Holger Kantz and Thomas Schreiber, Nonlinear Time Series Analysis
• Robert Shumway and David Stoffer, Time Series Analysis and Its Applications
• Norbert Wiener
  • Extrapolation, Interpolation and Smoothing of Stationary Time Series
  • Cybernetics

Recommended, closeups:
• Jochen Bröcker and Ulrich Parlitz, "Analyzing communication schemes using methods from nonlinear filtering," Chaos 13 (2003): 195--208
• A. E. Brockwell, A. L. Rojas and R. E. Kass, "Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering", Journal of Neurophysiology 91 (2004): 1899--1907 [Very nice, especially since they've combining data from multiple experiments. It is a little disappointing that they set up a state-space model, but then only use the state to enforce a kind of weak continuity constraint on the decoding, rather than trying to capture the actual computations going on. But I should talk to them about that... Appendix A gives a very clear and compact explanation of particle filtering.]
• Olivier Cappé "Online EM Algorithm for Hidden Markov Models", Journal of Computational and Graphical Statistics 20 (2011): 728--749, arxiv:0908.2359
• Pavel Chigansky and Ramon van Handel, "A complete solution to Blackwell's unique ergodicity problem for hidden Markov chains", Annals of Applied Probability 20 (2010): 2318--2345
• R. W. R. Darling, "Geometrically Intrinsic Nonlinear Recursive Filters," parts I and II, UCB technical reports 494 and 512
• P. Del Moral and L. Miclo, "Branching and Interacting Particle Systems Approximations of Feynman-Kac Formulae with Applications to Non-Linear Filtering", in J. Azema, M. Emery, M. Ledoux and M. Yor (eds)., Semainaire de Probabilites XXXIV (Springer-Verlag, 2000), pp. 1--145 [Postscript preprint. Looks like a trial run for Del Moral's book.]
• Uri T. Eden, Loren M. Frank, Riccardo Barbieri, Victor Solo and Emery N. Brown, "Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering", Neural Computation 16 (2005): 971-988 [Interesting development of filtering methods for point processes, beyond the neural application]
• Robert J. Elliott, Lakhdar Aggoun and John B. Moore, Hidden Markov Models: Estimation and Control
• Gregory L. Eyink, "A Variational Formulation of Optimal Nonlinear Estimation," physics/0011049 [Nice connections between optimal estimation (assuming a known form for the underlying stochastic process), nonequilibrium statistical mechanics, and large deviations theory, leading to tractable-looking numerical schemes.]
• Edward Ionides, "Inference and Filtering for Partially Observed Diffusion Processes" [PDF preprint]
• Jayesh H. Kotecha and Petar M. Djuric, "Gaussian Particle Filtering", IEEE Transactions on Signal Processing 51 (2003): 2592--2601
• M. L. Kleptsyna, A. Le Breton and M.-C. Roubaud, "Parameter Estimation and Optimal Filtering for Fractional Type Stochastic Systems", Statistical Inference for Stochastic Processes 3 (2000): 173--182
• Leonard A. McGee and Stanley F. Schmidt, "Discovery of the Kalman Filter as a Practical Tool for Aerospace and Industry", NASA Technical Memorandum 86847 (1985) [How we learned to aim for the stars and/or hit London. Free PDF.]
• V. V. Prelov and E. C. van der Meulen, "On error-free filtering of finite-state singular processes under dependent distortions", Problems of Information Trasmission 49 (2007): 271--279 ["We consider the problem of finding some sufficient conditions under which causal error-free filtering for a singular stationary stochastic process X = {X_n} with a finite number of states from noisy observations is possible. For a rather general model of observations where the observable stationary process is absolutely regular with respect to the estimated process X, it is proved (using an information-theoretic approach) that under a natural additional condition, causal error-free (with probability one) filtering is possible."]

To read:
• Lakhdar Aggoun and Robert Elliott, Measure Theory and Filtering: Introduction with Applications
• Francis Alexander, Gregy Eyink and Juan Restrepo, "Accelerated Monte-Carlo for Optimal Estimation of Time Series" = Journal of Statistical Physics 119 (2005): 1331--1345 [PDF]
• Shun-ichi Amari, "Estimating Functions of Independent Component Analysis for Temporally Correlated Signals," Neural Computation 12 (2000): 2083--2107
• Alan Bain and Dan Crisan, Fundamentals of Stochastic Filtering [blurb]
• Thomas Bengtsson, Peter Bickel, Bo Li, "Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems", pp. 316--334 in Deborah Nolan and Terry Speed (eds.), Probability and Statistics: Essays in Honor of David A. Freedman
• T. Bohlin, "Information pattern for linear discrete-time models with stochastic coefficients," IEEE Transactions on Automatic Control 15 (1970): 104--106 [On recursively-computable sufficient statistics]
• D. Brigo, B. Hanzon and F. LeGland, "A differential geometric approach to nonlinear filtering: the projection filter," IEEE Transactions on Automatic Control 43 (1998): 247--252 W. Bulatek, M. Lemanczyk and E. Lesigne, "On the Filtering Problem for Stationary Random^2$-Fields", IEEE Transactions on Information Theory 51 (2005): 3586--3593
• Emmanuel Candes and Terence Tao, "Near Optimal Signal Recovery from Random Projections and Universal Encoding Strategies", math.CA/0410542
• Carlos M. Carvalho, Michael S. Johannes, Hedibert F. Lopes, and Nicholas G. Polson, "Particle Learning and Smoothing", Statistical Science 25 (2010): 88--106
• Pavel Chigansky
  • "On exponential stability of the nonlinear filter for slowly switching Markov chains", math.PR/0411596
  • "An ergodic theorem for filtering with applications to stability", math.PR/0404515
• Pavel Chigansky and Robert Liptser
  • "Stability of nonlinear filters in nonmixing case", math.PR/0304056 = Annals of Applied Probability 14 (2004): 2038--2056
  • "What is always stable in nonlinear filtering?", math.Pr/0504094
• Alexandre J. Chorin and Paul Krause, "Dimensional reduction for a Bayesian filter", Proceedings of the National Academy of Sciences 101 (2004): 15013--15017 [If I understand their abstract correctly, they're basically saying that you only have to worry about uncertainties along the expanding directions of the dynamics --- uncertainty along the contracting directions is going to go away anyway! Probably it's not that simple...]
• Noel Cressie, Tao Shi, and Emily L. Kang, "Fixed Rank Filtering for Spatio-Temporal Data", Journal of Computational and Graphical Statistics (2010) forthcoming
• Irene Crimaldi and Luca Pratelli, "Two inequalities for conditional expectations and convergence results for filters", Statistics and Probability Letters 74 (2005): 151--162
• M. H. A. Davis and I. Marcus, "An Introduction to nonlinear filtering," pp. 53--75 in M. Hazewinkel and J. C. Willems (eds.), Stochastic Systems: The Mathematics of Filtering and Identification and Applications
• M. H. A. Davis and P. Varaiya, "Information states for linear stochastic systems," J. Math. Anal. Appl. 37 (1972): 384--402
• Pierre Del Moral
  • "Measure-Valued Processes and Interacting Particle Systems. Application to Nonlinear Filtering Problems", The Annals of Applied Probability 8 (1998): 438--495
  • Feynman-Kac Formulae: Genealogical and Interacting Particle Systems [This looks really, really cool]
• G. B. DiMasi and L. Stettner, "Ergodicity of hidden Markov models", Mathematics of Control, Signals, and Systems 17 (2005): 269--296 [Includes consideration of the ergodicity of filters for the HMM]
• C. T. J. Dodson and H. Wang, "Iterative Approximation of Statistical Distributions and Relation to Information Geometry", Statistical Inference for Stochastic Processes 4 (2001): 307--318 ["optimal control of stochastic processes through sensor estimation of probability density functions is given a geometric setting via information theory and the information metric."]
• F. Douarche, L. Buisson, S. Ciliberto and A. Petrosyan, "A Simple Denoising Technique", physics/0406055
• Randal Douc, Olivier Cappé and Eric Moulines, "Comparison of Resampling Schemes for Particle Filtering", cs.CE/0507025
• Randal Douc, Gersende Fort, Eric Moulines and Pierre Priouret, "Forgetting of the initial distribution for Hidden Markov Models", math.ST/0703836
• Randal Douc, Aurelien Garivier, Eric Moulines, Jimmy Olsson, "On the Forward Filtering Backward Smoothing particle approximations of the smoothing distribution in general state spaces models", arxiv:0904.0316
• Randal Douc and France E. Moulines, "Limit theorems for weighted samples with applications to Sequential Monte Carlo Methods", math.ST/0507042 [With application to state-space filtering]
• Gregory L. Eyink and Juan M. Restrepo, "Most Probable Histories for Nonlinear Dynamics: Tracking Climate Transitions", Journal of Statistical Physics 101 (2000): 459--472 [PDF]
• Gregory L. Eyink, Juan M. Restrepo and Francis J. Alexander, "A Statistical-Mechanical Approach to Data Assimilation"
  1. "Analysis Approximations" [PDF]
  2. "Evolution Approximations" [PDF]
  3. "Numerical Algorithms" [PDF]
• Paul Fearnhead, Omiros Papaspiliopoulos, Gareth Roberts, "Particle Filters for Partially Observed Diffusions", arxiv:0710.4345
• R. M. Fernandez-Alcala, J. Navarro-Moreno, and J. C. Ruiz-Molina, "A Unified Approach to Linear Estimation Problems for Nonstationary Processes", IEEE Transactions on Information Theory 51 (2005): 3594--3601
• B. Fristedt, N. Jain and N. Krylov, Filtering and Prediction: A Primer [Blurb]
• Ramazan Gencay, Faruk Selcuk and Brandon Whitcher, An Introduction to Wavlets and Other Filtering Methods in Finance and Economics
• Arnaud Guillin, Randal Douc and Jamal Najim, "Moderate Deviations for Particle Filtering", math.PR/0401058 = Annals of Applied Probability 15 (2005): 587--614
• Dong Guo, Xiaodong Wang and Rong Chen, "New sequential Monte Carlo methods for nonlinear dynamic systems", Statistics and Computing 15 (2005): 135--147
• A. Hannachi, "Probabilitic-based Approach to Optimal Filtering", Physical Review E 61 (2000): 3610--3619
• Simon Haykin, José C. Príncipe, Terrence J. Sejnowski and John McWhirter, New Directions in Statistical Signal Processing: From Systems to Brains [Blurb]
• M. Hazewinkel and S. I. Marcus, "On Lie algebras and finite-dimensional filtering" Stochastics 7 (1982): 29--62
• M. Hazewinkel and J. C. Willems (eds.), Stochastic Systems: The Mathematics of Filtering and Identification and Applications
• A. Inoue, Y. Nakano and V. Anh, "Linear filtering of systems with memory", math.PR/0407454
• Michael T. Johnson and Richard J. Povinelli, "Generalized phase space projection for nonlinear noise reduction", Physica D 201 (2005): 306--317
• Kevin Judd, "Failure of maximum likelihood methods for chaotic dynamical systems", Physical Review E 75 (2007): 036210 [He means failure for state estimation, not parameter estimation. I wonder if this isn't linked to the old Fox and Keizer papers about amplifying fluctuations in macroscopic chaos?]
• Kevin Judd and Leonard A. Smith
  • "Indistinguishable States I. Perfect Model Scenario", Physica D 151 (2001): 125--141
  • "Indistinguishable States II. The Imperfect Model Scenario", Physica D 196 (2004): 224--242
• Kevin Judd and Thomas Sternler, "Failures of sequential Bayesian filters and the successes of shadowing filters in tracking of nonlinear deterministic and stochastic systems", Physical Review E 7 (2009): 066206
• Kay, Fundamentals of Statistical Signal Processing [2 vols.]
• R. Khasminskii, "Nonlinear Filtering of Smooth Signals", Stochastics and Dynamics 5 (2005): 27--35
• Sangil Kim, Greg Eyink, Frank Alexander, Juan Restrepo and Greg Johnson, "Ensemble Filtering for Nonlinear Dynamics", Monthly Weather Reveiw 131: 2586--2594 [PDF]
• Arthur J. Krener, "The Convergence of the Extended Kalman Filter," math.OC/0212255, also A. Rantzer and C. I. Byrnes (eds.), Directions in Mathematical Systems Theory and Optimiazation (Berlin: Springer-Verlag, 2002): 173--182
• H. J. Kushner
  • "On the differential equations satisfied by conditional probability densities of Markov processes, with applications," J. SIAM Control A2 (1962): 106--119
  • "Approximation to Optimal Nonlinear Filters," IEEE Trans. Auto. Contr. 12 (1967): 546--556
  • Probability Methods for Approximations in Stochastic Control and for Elliptic Equations
• Sylvain Le Corff and Gersende Fort, "Online Expectation Maximization based algorithms for inference in hidden Markov models", arxiv:1108.3968
• Francois LeGland and Nadia Oudjane
  • "Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters", Annals of Applied Probability 14 (2004): 144--187
  • "A roubstification approach to stability and to uniform particle approximation of nonlinear filters: the example of pseudo-mixing signals", Stochastic Processes and Their Applications 106 (2003): 279--316
• John M. Lewis, S. Lakshmivarahan and Sudarshan Dhall, Dynamic Data Assimilation: A Least Squares Approach [Blurb]
• Robert S. Liptser and Albert N. Shiryaev, Statistics of Random Processes [2 vols., get 2nd edition]
• Xiaodong Luo, Jie Zhang and Michael Small, "Optimal phase space projection for noise reduction", nlin.CD/0506011
• Andrew J. Majda and Marcus J. Grote, "Explicit off-line criteria for stable accurate time filtering of strongly unstable spatially extended systems", Proceedings of the National Academy of Sciences (USA) 104 (2007): 1124--1129
• W. P. Malcolm, R. J. Elliott and M. R. James, "Risk-Sensitive Filtering and Smoothing for Continuous-Time Markov Processes", IEEE Transactions on Information Theory 51 (2005): 1731--1738
• Inés P. Mariño, Joaquín Míguez, and Riccardo Meucci, "Monte Carlo method for adaptively estimating the unknown parameters and the dynamic state of chaotic systems", Physical Review E 79 (2009): 056218
• Sanjoy K. Mitter and Nigel J. Newton, "Information and Entropy Flow in the Kalman-Bucy Filter", Journal of Statistical Physics 118 (2005): 145--176 [This looks rather strange, from the abstract, but potentially interesting...]
• Jun Morimoto and Kenji Doya, "Reinforcement Learning State Estimator", Neural Computation 19 (2007): 730--756
• Jose M. F. Moura and Sanjoy K. Mitter, "Identification and Filtering: Optimal Recursive Maximum Likelihood Approach" [1986 technical report from MIT, found looking for something else, original URL now lost --- presumably since published. Memo to self: (1) definitely read this; (2) look up publication.]
• D. Napoletani, C. A. Berenstein, T. Sauer, D. C. Struppa and D. Walnut, "Delay-Coordinates Embeddings as a Data Mining Tool for Denoising Speech Signals", physics/0504155
• V. Olshevsky and L. Sakhnovich, "Matched Filtering for Generalized Stationary Processes", IEEE Transactions on Information Theory 51 (2005): 3308--3313
• Jimmy Olsson, Olivier Cappe, Dandal Douc and Eric Moulines, "Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models", math.ST/0609514
• Jimmy Olsson, Jonas Ströjby, "Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators", arxiv:1008.2886
• Edward Ott, Brian R. Hunt, Istvan Szunyogh, Matteo Corazza, Eugenia Kalnay, D. J. Patil, and James A. Yorke, "Exploiting Local Low Dimensionality of the Atmospheric Dynamics for Efficient Ensemble Kalman Filtering," physics/0203058
• E. Ott, B. R. Hunt, I. Szunyogh, A. V. Zimin, E. J. Kostelich, M. Corazza, E. Kalnay, D.J. Patil and J.A. Yorke, "Estimating the state of large spatio-temporally chaotic systems", Physics Letters A 330 (2004): 365--370
• Francescco Paparella, "Filling gaps in chaotic time series", Physics Letters A 346 (2005): 47--53
• Anastasia Papavasiliou, "Particle Filters for Multiscale Diffusions", arxiv:0710.5098
• W. J. Runggaldier and F. Spizzichino, "Sufficient conditions for finite dimensionality of filters in discrete time: A Laplace transform-based approach," Bernoulli 7 (2001): 211--221
• Boris Ryabko, Daniil Ryabko, "Confidence Sets in Time--Series Filtering", arxiv:1012.3059
• Simo Särkkä and Tommi Sottinen, "Application of Girsanov Theorem to Particle Filtering of Discretely Observed Continuous-Time Non-Linear Systems", arxiv:0705.1598
• G. Sawitzki, "Finite-dimensional filters in discrete time," Stochastics 5 (1981): 107--114
• Steven T. Smith, "Covariance, Subspace, and Intrinsic Cramer-Rao Bounds", IEEE Transactions on Signal Processing forthcoming [Preprint kindly provided by Dr. Smith]
• Victor Solo and Xuan Kong, Adaptive Signal Processing Algorithms: Stability and Performance
• D. Sornette and K. Ide, "The Kalman-Levy filter," cond-mat/0004369
• R. L. Stratonovich
  • "Conditional Markov Processes," Theoretical Probability and Its Applications 5 (1960): 156--178
  • Conditional Markov Processes and Their Application to the Theory of Optimal Control
• Vladislav B. Tadic and Arnaud Doucet, "Exponential forgetting and geometric ergodicity for optimal filtering in general state-space models", Stochastic Processes and their Applications 115 (2005): 1408--1436
• Xin Thomson Tong, Ramon van Handel, "Ergodicity and stability of the conditional distributions of nondegenerate Markov chains", arxiv:1101.1822
• Ramon van Handel
  • "Observability and nonlinear filtering", Probability Theory and Related Fields 145 (2009): 35--74, arxiv:0708.3412
  • "Uniform Time Average Consistency of Monte Carlo Particle Filters", Stochastic Processes and their Applications 119 (2009): 3835--3861, arxiv:0812.0350
• T. Weissman, "How to Filter an `Individual Sequence with Feedback'", IEEE Transactions on Information Theory 54 (2008): 3831--3841
• J. C. Willems, "Some remarks on the concept of information state," pp. 285--295 in O. L. R. Jacobs (ed.), Analysis and Optimization of Stochastic Systems